1. Field of the Invention
The invention concerns a method for analysis of correspondences in image data sets and a device suitable for carrying out the process as set forth below.
2. Description of Related Art
In the processing of image data relating to moving scenes, in particular in the recognition and tracking of objects located therein, it is necessary to identify image points or, as the case my be, image areas, which correspond to each other in the separate chronologically sequential image data sets.
It is however difficult, particularly with monocular camera systems, to recognize objects from the image data obtained from the environment and to measure their movement parameters. One possibility is provided however by the analysis of the optical flow of objects within the image data. The optical flow describes the displacement of an image point of an object recorded at time i relative to the image points recorded at time j. The problem which occurs herein is comprised of determining which of the image points at moment i correspond with which image points of the camera image at moment j, that is, belongs to the same object. Hypotheses are set up in connection with this correspondence problem to be solved, regarding which pair of image points (image pixels) from the image at moment i and from the image at moment j correspond to each other. These hypotheses are then recorded in a hypothesis list for further processing, such as geometric object generation and object monitoring or tracking.
In the past years a large number of individual algorithms for optical flow have been developed, of which the most recognized are compared in an overview article by Barron et al. Barron distinguishes between:                Differential techniques, in which the optical flow is computed on the principle of location and time limited intensity changes (“spatial temporal derivatives of image intensities”) of the image points,        Matching—techniques, in which the change in position of objects mostly including multiple image points are observed in defined time intervals, and on the basis of this displacement of the these objects their speed and therewith the optical flow is determined,        Energy—based techniques, in which the computation of the optical flow is based on the output energy of speed—optimal filters. These types of techniques are also referred to as frequency based techniques, since the speed adapted filters are defined in their frequency range (Fourier domain),        Phase based techniques, which are based on the principle, that speed in image data are reproduced in the phase relationship of the output signal of band past filters.        
The known methods are based, as a rule, on computationally intensive correlation extensions and are in general only capable of measuring small displacements of objects (small optical flow) from one recorded image to the next. In particular, when using this type of algorithm in image recognition in motor vehicles, there occurs the problem, that the available controlled devices only have limited computational resources, in particular in the case of an aggressive steering movement or a high vehicle speed, large optical flows occur in the image data.
Besides the use of correspondence analysis in the determination of the optical flow of objects from chronologic occurring sequential image data sets, it is also necessary in stereo image processing to identify, in images recorded at essentially the same moment from different angles of view, those data areas which correspond with each other. The areas identified as corresponding are then associated with an object, so that from the known geometric relationship of the camera position and direction of view the distance and position of the object can be determined. Since the functional principle of the stereo image processing is based particularly thereupon, that one and the same object is recorded in essentially simultaneously recorded image data at different locations, the correspondence analysis represents the computationally intensive part of the image evaluation and it is the capacity limiting factor, in particular in applications in which only a limited amount of the computation resources can be made available.